Feng Liu (Lecturer at UTS-AAII)


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Feng Liu

Feng Liu, Ph.D.

Lecturer (eqiv. Assistant Professor) @ DeSI Lab,
Australian Artificial Intelligence Institute, UTS

Visiting Scientist @ Imperfect Information Learning Team,
RIKEN Center for Advanced Intelligence Project (RIKEN-AIP)

Address: Level 12, UTS Central,
61 Broadway, Ultimo, Sydney, NSW2007, Australia.
E-mail: feng.liu [at] uts.edu.au or feng.liu.1990 [at] ieee.org
[Google Scholar] [Github]


Biography

    I am a machine learning researcher with research interests in hypothesis testing and trustworthy machine learning. My long-term goal is to develop trustworthy intelligent systems that can learn reliable knowledge from massive related but different domains automatically. Researching hypothesis testing and trustworthy machine learning is the first step for achieving my long-term goal.

    I am currently a Lecturer (Assistant Professor) at Decision Systems and e-Service Intelligence (DeSI) Lab, Australian Artificial Intelligence Institute (AAII), University of Technology Sydney (UTS), Australia. I am also a Visiting Scientist at RIKEN-AIP, Japan. I was the recipient of the Australian Laureate postdoctoral fellowship. I received my Ph.D. degree in computer science at UTS-AAII in 2020, advised by Dist. Prof. Jie Lu and Prof. Guangquan Zhang.

    I was a research intern at the RIKEN-AIP, working on the robust domain adaptation project with Prof. Masashi Sugiyama, Dr. Gang Niu and Dr. Bo Han. I visited Gatsby Computational Neuroscience Unit at UCL and worked on the hypothesis testing project with Prof. Arthur Gretton, Dr. Danica J. Sutherland and Dr. Wenkai Xu.

    I have served as a senior program committee (SPC) member for ECAI and program committee (PC) members for NeurIPS, ICML, ICLR, AISTATS, ACML, AAAI, IJCAI, CIKM, ECAI and so on. I also serve as reviewers for many academic journals, such as IEEE-TPAMI, IEEE-TNNLS, IEEE-TFS, PR, AMM and so on. I received the Outstanding Reviewer Award of ICLR (2021), the UTS-FEIT HDR Research Excellence Award (2019), the Best Student Paper Award of FUZZ-IEEE (2019) and the UTS Research Publication Award (2018).


Research Interests

    My research interests lie in hypothesis testing and trustworthy machine learning. Specifically, my current research work center around three major topics:
  • Two-sample Testing: Measuring the discrepancy between two data distributions, a fundamental problem in machine learning and statistics.

  • Transfer Learning: Leveraging the knowledge from domains with abundant labels (i.e., source domains)/pre-trained models (i.e., source models) to complete classification/clustering tasks in an unlabeled domain (i.e., target domain).

  • Defending against Adversarial Attacks: Detecting adversarial attacks (i.e., adversarial attack detection); Training a robust model against future adversarial attacks (i.e., adversarial training)


Research Experience

  • Visiting Researcher (August 2019--November 2019)

  • Gatsby Computational Neuroscience Unit, UCL, London, UK
    Advisor: Prof. Arthur Gretton
    Collaborators: Dr. Danica Sutherland, Wenkai Xu
    Project: Learning Deep Kernels for Two Sample Test

  • Research Intern (March 2019--July 2019)

  • Imperfect Information Learning Team, RIKEN-AIP, Tokyo, Japan
    Advisor: Prof. Masashi Sugiyama
    Collaborators: Dr. Gang Niu and Dr. Bo Han
    Project: Wildly Unsupervised Domain Adaptation

  • Research Assistant (July 2015--July 2016)

  • Institute of Statistical Science, School of Statistics,
    Dongbei University of Finance and Economics, Dalian, China
    Advisor: Prof. Ping Jiang
    Collaborators: Prof. Jianzhou Wang, Yiliao Song
    Project: Time Series Prediction and Interpolation

  • Research Intern (May 2014--September 2014)

  • State Key Laboratory of Numerical Modeling for Atmospheric Sciences and
    Geophysical Fluid Dynamics (LASG),
    Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
    Advisor: Senior Scientist Zhenhai Guo
    Collaborators: Dr. Xia Xiao, Dr. Jing Zhao, Dr. Zhongyue Su
    Project: Forecasting Long-Term Wind Speed via NWP ensembles


Education

  • Ph.D. in Computer Science (November 2020)

  • Faculty of Engineering and Information Technology,
    University of Technology Sydney, Sydney, Australia.
    Supervised by Dist. Prof. Jie Lu and Prof. Guangquan Zhang

  • Master of Science (June 2015)

  • School of Mathematic and Statistics, Lanzhou University, Lanzhou, China
    Supervised by Prof. Jianzhou Wang

  • Bachelor of Science (June 2013)

  • School of Mathematic and Statistics, Lanzhou University, Lanzhou, China


Sponsors

Australian Research Council